Worldview-3 (WV-3) is the industry's first super-spectral, Very-High Resolution (VHR) commercial satellite, with sensors having panchromatic resolutions of 31 cm; Multispectral, also known as Visible Near Infrared (VNIR), resolutions of 1.2 m; and Short Wave Infrared (SWIR) resolutions of 7.5 m (3.72 m native), as shown in Table 1 below. These sensors have a geo-positional accuracy of less than 3.5 m CE90. The WV-3 has an average revisit time of less than 1 day, and it can collect up to 680,000 km2 per day.
TABLE 1Multispectral response of the Very High Resolution (VHR)WorldView-3 (WV-3) sensors.Panchromatic: 450-800 nm8 Multispectral:Coastal: 400-450 nmRed: 630-690 nmBlue: 450-510 nmRed Edge: 705-745 nmGreen: 510-580 nmNear-IR1: 770-895 nmYellow: 585-625 nmNear-IR2: 860-1040 nm8 SWIR Bands:SWIR-1:1195-1225 nmSWIR-5:2145-2185 nmSWIR-2:1550-1590 nmSWIR-6:2185-2225 nmSWIR-3:1640-1680 nmSWIR-7:2235-2285 nmSWIR-4:1710-1750 nmSWIR-8:2295-2365 nm
It is well known that images with multiple bands have better discrimination than images with fewer bands. For example, a Red-Green-Blue (R-G-B) color image has much better discrimination capability than gray-level images. Since the WV-3 data have sixteen bands, excluding the panchromatic band, with different resolutions, it will be ideal to fuse them to generate sixteen VHR images. Although there have been many algorithms in the literature for fusing images with different bands, this field is still evolving with new algorithms introduced from time to time.
As discussed in a paper, “Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 2, February 2005, by H. Kwon, and N. M. Nasrabadi, the Kernel RX-algorithm is a generalization of the well-known anomaly detection algorithm, known as Reed-Xiaoli (RX) algorithm. When the kernel distance function is defined as the dot product of two vectors, Kernel RX is the same as RX. While Kernel RX is more flexible than RX, it is significantly slower than RX. A novel algorithm can perform a fast approximation of Kernel RX in the present invention, as disclosed in an article, “A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images,” IEEE Trans. Geoscience and Remote Sensing, Volume: 54, Issue: 11, pp. 6497-6504, November 2016, by J. Zhou, C. Kwan, B. Ayhan, and M. Eismann. The novel algorithm is based on clustering, called Cluster Kernel RX (CKRX). As a matter of fact, CKRX is a generalization of Kernel RX (KRX), i.e. CKRX is reduced to Kernel RX under some specific settings.
The basic idea of CKRX is: first cluster the background points and then replace each point with its cluster's center. After replacement, the number of unique points is the number of clusters, which can be very small comparing to the original point set. Although the total number of points does not change, the computation of the anomaly value can be simplified using only the unique cluster centers, which improves the speed by several orders of magnitudes.
The paper mentioned above showed that some Receiver Operating Characteristics (ROC) curves were obtained by using actual hyperspectral images from the Air Force (AF). Many algorithms implemented and compared in that paper. Also, FIG. 9 of the present invention shows the ROC curves, showing that KRX and CKRX gave excellent performance, as their ROC curves almost reach ideal performance.
In surface characterization, accurate material classification is important for mapping out the Earth surface. There are some existing classification algorithms as shown in the article, “A Novel Approach for Spectral Unmixing, Classification, and Concentration Estimation of Chemical and Biological Agents,” IEEE Trans. Geoscience and Remote Sensing, pp. 409-419, vol. 44, no. 2, February 2006, by C. Kwan, B. Ayhan, G. Chen, C. Chang, J. Wang, and B. Ji.
In remote sensing domain, a common and successful approach to achieving super resolution is pan-sharpening. Pan-sharpening is an image fusion technique which uses a high resolution single band panchromatic image and low resolution multi-spectral image to produce high resolution multi-spectral images. Compared to multi-view based and example based super-resolution technique, pan-sharpening can produce much higher resolution data and is much more reliable and accurate. The pan-sharpening idea can also be applied to hyperspectral images, as disclosed in some articles, for example, “Hyperspectral Image Super-Resolution: A Hybrid Color Mapping Approach,” SPIE Journal of Applied Remote Sensing, September, 2016, by J. Zhou, C. Kwan, and B. Budavari; and “Resolution Enhancement for Hyperspectral Images: A Super-Resolution and Fusion Approach,” accepted by International Conference Acoustics, Speech, and Signal Processing 2017, by C. Kwan, J. H. Choi, S. Chan, J. Zhou, and B. Budavari. In the present invention, a novel approach which extends the idea of pan-sharpening by using multiple high resolution bands to reconstruct high resolution hyperspectral image was developed. The motivation is practical: there are many satellite sensors or airborne sensors which take high resolution color images. For instance, the resolution of IKONOS color image data is 0.5 meter.
Sparsity based classification algorithm to rock type classification such as the method described in an article, “Burn Scar Detection Using Cloudy MODIS Images via Low-rank and Sparsity-based Models,” IEEE Global Conference on Signal and Information Processing, Washington, D.C., Dec. 7-9, 2016, by M. Dao, C. Kwan, B. Ayhan, and T. Tran.
The Extended Yale B face database, as disclosed in an article, “Locally Adaptive Sparse Representation for Detection, Classification, and Recognition,” Signals and Systems Area Seminar, Johns Hopkins University, Baltimore Md., by T. D. Tran, has been used for performance evaluation. In addition to frontal face images, the present invention introduced rotation effects to the test face images to examine the robustness of the global (whole face) and local (blocks of the face image) versions of the method. The Yale B database contains face images with different illuminations, which are very challenging.
Support Vector Machine (SVM) and non-deep Neural Networks (NN) have been used in many pattern classification applications. However, the present invention believes there is a lot of room for further improvement. This is because SVM and non-deep NN have only one or two layers of tunable parameters. Since pattern recognition and concentration estimation are complex and involve sophisticated features, SVM and non-deep NN may be restricted in achieving high classification rate.